Credit scorecards are empirically derived statistical models that can be used to predict credit repayment performance of applicants based on their observable characteristics. For example, the credit scorecards may be based on applicant information available at the time of the application and performance over time, usually one to two years. Credit scorecards are typically developed based on known performance of a given population, then used to make decisions on future credit applicants whose performance is not yet known. Credit scorecards are used almost universally by financial institutions for bankcard and other unsecured loan underwriting.
Credit scorecards are periodically rebuilt, usually every five to seven years, to remain optimally predictive in light of changing economic, demographic, behavioral, and marketing conditions. Reject inference is a method for improving the quality of a scorecard based on the use of data contained in rejected loan applications. Prior reject inference methods have relied on scoring-based reject inference or evidence of other credit account (i.e., tradeline data) performance as an indicator for future credit performance. Financial institutions typically redevelop their scorecards based on the payment and default history of their credit applicants, as that population best reflects the targeted market of the institution, and the expected performance of a particular financial product. However, scorecard redevelopment can be complicated by the fact that a prior credit scorecard may have been used to make credit decisions when the existing customer population first applied for a new account. Some of the credit applicants may be rejected by the old scorecard because their predicted credit default rate was too high to be profitable under the terms of a particular financial product. Such applicants can be rejected and sent an Adverse Action letter informing them of their failure to pass credit underwriting. The letter may also include the primary reasons for that failure.
When credit applicants are declined, they may not have the opportunity to generate further information related to payment or credit default performance. Due to a lack of performance information, rejected applications are sometimes excluded from scorecard redevelopment, with the result that the new scorecard is not trained to detect the high risk segments that the old scorecard successfully rejected. This can result in a new scorecard that results in unexpectedly high credit losses.